A business or enterprise may store information about various items in the form of electronic records. For example, a company might have an employee database where each row in the database represents a record containing information about a particular employee (e.g., the employee's name, date of hire, and salary). Moreover, different electronic records may actually be related to a single item. For example, a human resources database and a sales representative database might both contain records about the same employee. In some cases, it may be desirable to consolidate multiple records to create a single data store that contains a single electronic record for each item represented in the database. Such a goal might be associated with, for example, an automated a data quality process application and/or a data steward that attempt to automatically recognize or match these records to create a correct “master” data store. Advantages associated with creating such a master data store might include increased efficiency through the enterprise and/or improved customer service. For example, when a sales representative retrieves a customer record, the master data store might include contact information that would have been missing if information from multiple records were not correctly matched and merged.
The matching and consolidation process in a data quality program can be a relatively time consuming and/or expensive task, especially when a substantial number of records (e.g., millions of records) and/or input data sources are involved. It can be difficult, moreover, to determine why certain records are automatically matched while others are not. For example, a data steward might be unsure why two very similar records were not automatically identified as a potential match.
Accordingly, methods and mechanisms for accurately and efficiently providing an understanding of how a data quality program operates may be desired.